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Abstrak :
ABSTRAK
Due to the situation of increasingly severe PM-10 pollution that adverse affects on humans and environment across the globe, the purpose of this work is to implement the optimal PM-10 forecast model as a basis tool in process of planing/controlling air pollution and public awareness apply to Chiang Mai city and surrounding area, in Northern Thailand. Accurate and reliable forecasting model are our goal. Due to the fuzzy feature of PM-10 as well as the high correlated hotspot during open burning and forest fires season of this study area, the adaptive neuro-fuzzy inference system (ANFIS)-based forecasting model has been statistically implemented as tool for daily mean PM-10 concentration estimation. For achieving more efficient and realistic model, the hotspot count among other meteorological parameters is utilized as the exogenous variable through the design and optimization. The forecasting performance evaluated in terms of the tradeoff between accuracy with regard to RMSE and MAE, computational complexity with respect to the multiplications per an execution, and reliablity through Akaike criterion information (AIC) is used to assess the forecast models. As forecasting results, the proposed ANFIS model with an integrated hotspots outperforms the other existing models.

Abstrak :
Indeks Sentimen Konsumen (ISK) banyak digunakan sebagai pengukur keyakinan pembelian di masa mendatang untuk memprediksi perilaku pembelian agregat di masa datang. Studi ini secara empiris membandingkan antara dua model proyeksi : model pengharapan (the expectation model) yang memasukkan ISK sebagai variabel penjelasan dan model tradisional (the traditional model) yang tidak memasukkan ISK. Kedua modal tersebut digunakan untuk mengestimasi permintaan agregat atas mobil baru di Amerika sejak 1976 sampai 1984. Hasil studi mengindikasikan bahwa ISK memiliki hubungan positif dengan penjualan mobil baru. Namun, berdasarkan kemungkinan kriteria dominan (the likelihood dominance criterion), model tradisional lebih baik dibandingkan model pengharapan. Selain itu, kemampuan memprediksi dari model pengharapan sedikit lebih rendah dibandingkan model tradisional. ...... The index of consumer sentiment (ICS) has been widely employed as a proxy for future buying confidence to predict future aggregate buying behavior. This study empirically compares two forecast models: the expectation model that includes the ICS as an explanatory variable and the traditional model that does not include the ICS. The models are employed to estimate the aggregate demand for new cars in the U.S. from 1976 to 1984. The results indicate that the ICS has a positive relation with new car sales. On the basis of the likelihood dominance criterion, however, the traditional model is preferred to the expectation model. Furthermore, the forecast ability of the expectation model is slightly inferior to the traditional model.